Weight assignment for fusion of prognostic estimators

ABSTRACT

A system for predicting remaining useful life of a component implements a set of estimation models that generate future damage estimates for the component. The system detects damage to the component and estimates the magnitude of the current damaged. An error processor estimates the between each future damage estimate and the magnitude of current damage. A weight calculator calculates weights for the future damage estimates, wherein each weight is inversely proportional to the error. A fusion processor applies the weights respectively to future damage estimates of the estimators and combines the weighted future damage estimates.

BACKGROUND

Prognostic estimation can be useful to estimate the remaining usefullife of various types of equipment. Remaining useful life estimatesallow equipment operators to make informed decisions about schedulingmaintenance, decommissioning equipment, and avoiding equipment downtime.

BRIEF SUMMARY

Some embodiments are directed to a system comprising an estimationprocessor configured to implement a set of diverse estimation modelsthat generate future damage estimates for a component. Each estimationmodel generating a future damage estimate, y_(i), for the component ateach of a plurality of life units, i, of the component based on one ormore anticipated stressors of the component. The system includes atleast one damage detector configured to detect damage to the component.A damage estimator is configured to estimate a magnitude of currentdamage, gt_(i), of the component for each life unit, i, based at leastin part on the detected damage. An error processor is configured toestimate an error, e_(i), between each future damage estimate, y_(i),and the magnitude of current damage, gt_(i), for each life unit, i. Aweight calculator calculates weights, w_(i), for the future damageestimates, each weight being inversely proportional to the error. Afusion processor is configured to combine the future damage estimates ofthe estimation models. The fusion processor applies the calculatedweights respectively to future damage estimates providing weightedfuture damage estimates and generates a combined future damage estimateby combining the weighted future damage estimates. The fusion processorpredicts a remaining useful life of the component based on the combinedfuture damage estimate.

According to some aspects, the system includes at least one model of thecomponent and the magnitude of current damage is based on estimationsobtained from the model. For example, the model may be a physics modelof the component.

In some aspects of the system, at each life unit, i, the error, e_(i),for each damage estimate, y_(i), is based on the equatione_(i)=(gt_(i)−y_(i))².

According to some aspects of the system, at each life unit, i, theerror, e_(i), is smoothed to filter the higher order changes of theerror. For example, the filter is an exponential moving average filterwith a smoothing constant, α.

According to some aspects at each life unit, i, the weight, w_(i), forthe future damage estimate, y_(i), is a maximum of errors of futuredamage estimates of the estimation models 1 to N, max(e₁, e₂, . . . ,e_(N)) for the life unit, i, divided by an absolute value of the error,|e_(i)| for the future damage estimate, y_(i).

In some implementations, the weight calculator is configured to skew theweights toward underestimation of the future damage.

The weight calculator can be configured to calculate relatively largerweights for damage estimates of estimation models that underestimate theremaining useful life of the component and calculate relatively smallerweights for damage estimates of estimation models that overestimate theremaining useful life of the component.

According to some aspects, at each life unit, i, the error e_(i) foreach damage estimate, y_(i), is based on the equation

$e_{i} = \left\{ \exp^{- \frac{y_{i} - {gt}_{i}}{{scalar}1}} \right\}$if y_(i)−gt_(i)<0, and

$\left\{ \exp^{- \frac{y_{i} - {gt}_{i}}{{scalar}2}} \right\},$otherwise.

For example, scalar1=10 and scalar2−0.4.

According to some scenarios, at each life unit, i, the weight, w_(i),for the future damage estimate, y_(i), is a maximum of errors of futuredamage estimates of the estimation models 1 to N, max(e₁, e₂, . . . ,e_(N)) for the life unit, i, divided by an absolute value of the error,|e_(i)| for the future damage estimate, y_(i).

In some implementations, the system includes a memory that storesknowledge of overestimation of future damage by at least one estimationmodel and knowledge of underestimation of future damage by at leastanother estimation model.

The weights for the first estimator can be based on magnitudes of theoverestimation of the future damage and the weights for the secondestimation model can be based on magnitudes of the underestimation ofthe future damage.

In some configurations, the estimation processor is configured to updateat each life unit at least some of the estimation models based on themagnitude of current damage prior to generating the future damageestimates.

The system can include a stage of life estimator configured identifymultiple stages of life of the component and to calculate the weightsbased on a current stage of life of the component. For example, thestage of life estimator may identify multiple stages of life of thecomponent. Each of the stages can be identified based on a particularmagnitude of current damage, gt_(i). The stage of life estimator may beconfigured to calculate the weights based on whether the current stageof life of the component is early life, middle life, or late life.

According to some aspects, the stage of life estimator is configured toidentify and early life stage and one or more later life stages. Theearly life stage is one in which the component has not experienced afault condition that exceeds a predetermined fault threshold. The laterlife stages are ones in which the component has experienced a faultcondition that exceeds a predetermined fault threshold.

According to some aspects, the system includes a reasoner processorconfigured to detect a fault condition of the component. The reasonerprocessor may also confirm the fault condition after detecting it, e.g.,to prevent spurious detection. For example, the reasoner processor mayconfirm the fault condition of the component by delaying a finalconfirmation of the fault condition for additional life units after thefault condition is initially detected to determine if the faultcondition persists. The reasoner processor may detect the faultcondition of the component using a first source of information andconfirm the fault condition using a second source of information that isdifferent from the first source of information.

The reasoner processor can calculate a prognostic horizon of theremaining useful life of the component in response to the faultcondition. The reasoner processor can be configured to detect the faultcondition based on the future damage estimates.

According to some aspects, the weight calculator is configured tocalculate the weights based on one or both of the prognostics horizonand a life stage of the component within the prognostic horizon. Forexample, the weight calculator may calculate the weights based on one ofmultiple stages of life of the component within the prognostic horizon.

The reasoner processor may be configured to update the prognostichorizon based on changes in anticipated stressors of the componentand/or the magnitude of current damage, gt_(i). The weight calculatorcan calculate the weights based on one of multiple stages of life of thecomponent that occur after the updated prognostic horizon.

In some implementations, a training subsystem configured to train atleast one estimation model. The training subsystem can be configured totrain at least one estimation model to overestimate the remaining usefullife of the component and/or to underestimate the remaining useful lifeof the component.

In some implementations, the estimation processor is configured todetect outlier future damage estimates. In these implementations, theweight calculator is configured to set the weights corresponding to theoutlier future damage estimates to zero.

Some embodiments are directed to a method of predicting a remaininguseful life of a component. A set of diverse estimation models thatgenerate future damage estimates is implemented for a component. Eachestimation model generates a future damage estimate, y_(i), for thecomponent at each of a plurality of life units, i, based on one or moreanticipated stressors of the component; The damage to the component isdetected and a magnitude of current damage, gt_(i), of the component foreach life unit, i, is estimated based at least in part on the detecteddamage. An error, e_(i), between each future damage estimate, y_(i), andthe magnitude of current damage, gt_(i), for each life unit, i, isestimated. Weights, w_(i), for the future damage estimates, arecalculated. Each weight is inversely proportional to the error. Thefuture damage estimates are combined. Combining the future damageestimates involves applying the calculated weights respectively tofuture damage estimates providing weighted future damage estimates andgenerating a combined future damage estimate by combining the weightedfuture damage estimates. The remaining useful life of the component ispredicted based on the combined future damage estimate. For example, themagnitude of current may be measured or estimated. Estimating themagnitude of the current damage can be based on a physics model of thecomponent.

According to some aspects, at each life unit, i, the error e_(i) foreach damage estimate, is based on the equation e_(i)=(gt_(i)−y_(i))².According to some aspects, at each life unit, i, the error, e_(i), is anexponential moving average with a smoothing constant, α. According tosome aspects, at each life unit, i, the weight, w_(i), for the futuredamage estimate, y_(i), is a maximum of errors of future damageestimates of the estimation model 1 to N, max(e₁, e₂, . . . , e_(N)) forthe life unit, i, divided by an absolute value of the error, |e_(i)| forthe future damage estimate, y_(i).

In some implementation, calculating the weights comprises skewing theweights toward overestimation of the future damage. For example,relatively larger weights can be calculated for the future damageestimates of estimation models that overestimate the future damage tothe component. Relatively smaller weights can be calculated for futuredamage estimates of estimation models that underestimate the futuredamage to the component.

The method can include storing knowledge of overestimation of the futuredamage by one or more first estimation models and knowledge ofunderestimation of the future damage by one or more second estimationmodels. The weights can be calculated based on the knowledge ofoverestimation or underestimation.

The method can include storing knowledge of magnitudes of overestimationof the future damage estimated by one or more first estimation modelsand knowledge of magnitudes of underestimation of the future damageestimated by one or more second estimation models. Weights for the firstestimation models can be calculated based on the magnitudes ofoverestimation of the future damage. Weights for the second estimationmodels can be based on the magnitudes of the underestimation of thefuture damage.

Prior to generating the future damage estimates the method can includeupdating at each life unit at least some of the estimation models basedon the magnitude of current damage.

The method may include identifying multiple stages of life of thecomponent and calculating the weights based on a current stage of lifeof the component. The method can include calculating a prognostichorizon of the remaining useful life and calculating the weights duringthe prognostic horizon. The prognostic horizon may be calculated inresponse to the detection of a fault condition. The method can includeconfirming the fault condition after it is detected. For example, thefault condition can be confirmed by delaying a final confirmation of thefault condition for additional life units after the initial detection ofthe fault so as to determine if the fault condition exists. For example,the fault condition can be detected using a first source of informationand can be confirmed using a second source of information that isdifferent from the first source of information. The fault condition canbe detected based on the future damage estimates.

According to some implementations of the method, a first process is usedto calculate weights before the prognostic horizon and a second processis used to calculate weights during the prognostic horizon period.Multiple stages of the component can be identified, before and/or duringthe prognostic horizon, wherein the weights are calculated based on thestage of life of the component. The prognostic horizon can be updatedbased on changes the anticipated stressors of the component. The weightsmay be re-calculated after the prognostics horizon is updated. Theprognostic horizon can be updated based on the magnitude of currentdamage, gt_(i).

According to some aspects of the method, at least one estimation modelis trained, e.g., by machine learning. The estimation model may begrained to overestimate or underestimate the future damage to thecomponent, for example.

According to some aspects, the method involves setting the weights ofone or more outlier future damage estimates to zero.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system for estimating remaining usefullife of a component in accordance with some embodiments;

FIG. 2 is a flow diagram illustrating a process for estimating remaininguseful life of a component in accordance with some embodiments;

FIG. 3 shows superimposed graphs of the future damage estimationtrajectories, damage, and fused damage estimation using a symmetricweighting approach obtained using systems and processes in accordancewith some embodiments;

FIG. 4 shows superimposed graphs of the squared error for the futuredamage estimations in accordance with some embodiments;

FIG. 5 shows superimposed graphs of the weights calculated for thefuture damage estimates using a symmetric weighting approach inaccordance with some embodiments;

FIG. 6 shows superimposed graphs of the future damage estimationtrajectories, actual damage, and fused damage estimation using aasymmetric weighting approach obtained using systems and processes inaccordance with some embodiments;

FIG. 7 shows superimposed graphs of the squared error for the futuredamage estimations in accordance with some embodiments;

FIG. 8 is shows a zoomed in portion of FIG. 7 ;

FIG. 9 shows superimposed graphs of the weights calculated for thefuture damage estimates using an asymmetric weighting approach inaccordance with some embodiments;

FIG. 10 shows superimposed graphs of the future damage estimationtrajectories, actual damage, and fused damage estimation with indicationof early, middle, and late stages of life in accordance with someembodiments;

FIG. 11 shows superimposed graphs of the weights calculated for thefuture damage estimates during early, middle, and late life stages inaccordance with some embodiments;

FIGS. 12A and 12B are flow diagrams illustrating a process fordetermining remaining useful life in accordance with some embodiments;

FIG. 13 shows superimposed graphs of the future damage estimates of theestimation models, the actual damage, and the fused damage estimationcalculated using first weights prior to a prognostic horizon and usingsecond weights after the prognostic horizon in accordance with someembodiments; and

FIG. 14 shows superimposed graphs of the weights used to determine thefused estimation of FIG. 13 .

The figures are not necessarily to scale. Like numbers used in thefigures refer to like components. However, it will be understood thatthe use of a number to refer to a component in a given figure is notintended to limit the component in another figure labeled with the samenumber.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The approaches described herein relate to the field of makingpredictions of remaining useful life (RUL) for some type of component,e.g., pump, valve, transformer, engine, medical system, structuralmember, battery, etc. Such predictions are particularly important in thefield of condition based maintenance (CBM) because this predictiveinformation allows the scheduling of maintenance at a time of minimaloperational disruption, thereby saving cost compared to unscheduled,reactive maintenance. For the purpose of scheduling maintenance andpreventing component failure it is useful to have an accurate estimationof remaining life of the component. Since component failure is still inthe future at the time of the estimation, the quality of the estimationcan vary. Increasing the prediction accuracy for any particularestimation model can be challenging. The disclosed approaches enhanceRUL estimation accuracy by considering a number of different types ofestimation models that estimate the remaining useful life of thecomponent. The outputs of the estimation models are aggregated toprovide a fused estimate that is on average more accurate than theestimate of any of the individual estimation models.

The remaining useful life of the component can be predicted based on anestimate of the future damage to the component. The estimators mayemploy fundamentally different approaches to provide the future damageestimates of the component. For example, one or more estimators mayoperate from first principles of the physics of the system and/or faultpropagation for given operating and environmental conditions to providethe future damage estimates. Alternatively or additionally, one or moreestimators may operate from models based on empirical data gainedthrough performing a number of experiments to provide the future damageestimates or from having collected trajectories of data relating to pastfailures.

Aggregating the future damage estimates produced by the estimatorsinvolves assigning weights to the future damage estimates. The weightsare applied to the estimates of future damage prior to aggregating theweighted estimates. Aggregating information from multiple estimators ismotivated by the fact that averaging a large number of informationsources often times tends towards the correct result. This is embodiedin the phenomenon of the “wisdom of the crowd” which gives the rightanswer more often than not—with the understanding that a right answercannot be guaranteed. Where the number of information points is small(i.e., lacking statistical significance), it may still be desirable toaggregate the information from different information sources. Thefundamental thinking is that knowledge about weaknesses of oneinformation source can be made up with knowledge about a strength ofanother information source. In that case, understanding the strengthsand weaknesses of the information sources allow these to be traded offappropriately through weighting. Therefore, finding and assigning theright weights to the information sources is useful for arriving at goodperformance.

In the context of estimating remaining useful life, some of theconsiderations are to make sure that outlier opinions (e.g., informationsources that appear to be diverging inexplicably from expected behavior)are treated with caution (and possibly given a much smaller weight).Often times, remaining life estimators behave well in some region of theremaining life estimation, and not so well in other regions. Inparticular, when end of life is being approached, individual estimatorsmay sometimes behave poorly because additional damage modes becomedominant which may not have been modeled explicitly or for which notenough examples have been collected for training purposes. This isundesired because a component that is close to end-of-life is beingtracked more closely and mitigating decisions are made based on theinformation at hand. Systems and methods for calculating dynamic weightassignment used in combining estimators for remaining useful life arediscussed below.

FIG. 1 is a block diagram of a system 100 configured to estimate theremaining useful life of a component 199 by combining the outputs of adiverse set of RUL estimation models 111, 112, 113, 114, which are alsoreferred to herein as “estimators.” An estimation processor 110 isconfigured to implement the estimators 111, 112, 113, 114, eachestimator 111, 112, 113, 114 providing estimates of future damage overthe remaining life of the component 199. Although in the block diagramof FIG. 1 , four estimators 111, 112, 113, 114 are illustrated, it willbe appreciated that the number, N, of diverse estimators implemented inthe system 100 can in general be fewer than four or more than four. Eachestimator 111, 112, 113, 114 may use a different algorithm to generatean estimation of future damage, y_(i), for the component 199 for eachlife unit, i. A life unit, i, can be a unit of time, a component cycle,of any other unit that can be used to quantify the consumption of thelife of the component 199. Each estimator 111, 112, 113, 114 generatesfuture damage estimates, y_(i), based on one or several stressors suchas anticipated load conditions of the component 199 and/or anticipatedenvironmental conditions of the component 199.

The system 100 includes at least one damage detector 120 configured todetect damage to the component 199. According to some embodiments, thedamage detector 120 can comprise one or more sensors configured torespectively sense one or more performance parameters of the component199 and to generate a signal representing those performance parameters.In one example, the component 199 may be a battery, the performanceparameter may be internal strain within the battery which is measured bya sensor placed within the battery. In another example, the performanceparameter may be battery voltage that is measured externally to thebattery, in yet other examples, the performance parameter may becorrosion of a structural member, the spall size within a bearing, or acrack length in a structural component.

The system 100 can include a damage estimator 130 that provides anestimate of the magnitude of current damage, gt_(i), of the componentfor each life unit, i, of the component based at least in part on thedetected damage. In some scenarios, the damage estimator 130 may trendthe performance parameters provided by the damage detector 120 andestimate damage based on trends. The damage estimator 130 may take basethe current damage estimate on information from sources other than thesensor 120, such as using a physics model 170 of the component 199,taking into account the past and present load conditions of thecomponent 199, the past and present environmental conditions of thecomponent 199 and/or other factors. The output of the damage estimatorrepresents the estimated current damage of the component for each lifeunit, gt_(i), which is also referred to as ground truth for that lifeunit.

An error processor 140 estimates an error, e_(i), between the futuredamage estimate, y_(i), of each of the estimators 111, 112, 113, 114 andthe magnitude of damage, gt_(i), produced by the damage estimator 130for each life unit, i. (The magnitude of damage, gt_(i), is alsoreferred to herein as ground truth.) For example, the error, e_(ni), forestimator n at life unit, i, can be written, e_(ni)=(gt_(i)−y_(ni)),where, in the illustrated example, n is an integer from 1 to 4, theinteger corresponding to one of the estimators 111, 112, 113, 114. Ingeneral, the error, e_(ni) is a measure of performance of the n^(th)estimator at the life unit, i.

A weight calculator 150 calculates weights, w_(ni), for the futuredamage estimates, y_(ni), for each estimator at each life unit, whereinthe weights are based on the estimated error. In some embodiments eachweight, w_(ni), is based on a reciprocal of the error.

According to some aspects, the estimation processor 110 may beconfigured to identify outlier future damage estimates provided ty theRUL estimators 111, 112, 113, 114. The outlier future damage estimatesmay be identified by comparing the future damage estimates to outliercriteria. The outlier criteria which can be based on sudden changes inthe trajectory produced by the estimator. For example, future damageestimates of an estimator having values that are too far away from thevalues of surrounding future damage estimates of the estimator may beidentified as outliers. When outlier future damage estimates areidentified, the weight calculator 150 can set the weights correspondingto the outlier future damage estimates to zero. According to someembodiments, the estimation processor 110 may determine that one of theestimators is consistently producing outlier future damage estimates. Inthis scenario, the estimation processor 110 may set all of the weightsfor the future damage estimates for the outlier estimator to zero or mayotherwise ignore the output of the outlier estimator.

A fusion processor 160 combines the future damage estimates of theestimators 111, 112, 113, 114. The combining may be accomplished bysumming or by other aggregation techniques. To combine the future damageestimates, the fusion processor 160 first applies the weights to thefuture damage estimates producing weighted future damage estimates,w_(ni)y_(ni), for estimators n=1 to N for each life unit, i. The fusionprocessor 160 then combines the weighted future damage estimates toproduce a fused future damage estimate. In one example, at life unit, i,the fused future damage estimate for this example may be written,f _(i) =w _(1i) y _(1i) +w _(2i) y _(2i) +w _(3i) y _(3i) +w _(4i) y_(4i).  [1]

The remaining useful life of the component 199 at each life unit, i, canbe predicted by the fusion processor 160 based on the fused damageestimate, f_(i).

FIG. 2 is a flow diagram of a process implemented by the system 100. Theprocess involves implementing 210 a set of diverse estimators thatgenerate future damage estimates for a component. Each estimator, n,generates a future damage estimate, y_(ni), for the component at eachlife unit, i, of the component. The estimators may determine the futuredamage estimates based on one or more stressors on the component suchanticipated load conditions and/or anticipated environmental conditions.The damage to the component is detected 220. The magnitude of thecurrent damage, gt_(i), of the component for each life unit, i, can bedetermined 230 based at least in part on the detected damage. The error,e_(ni), between the future damage estimate, y_(ni), and the magnitude ofthe current damage, gt_(i), at each life unit, i, is estimated 240.Weights, w_(ni), for each future damage estimate, y_(ni), are calculated250. The future damage estimates of all the estimators are fused byapplying 260 the calculated weights respectively to the future damageestimates at each life unit, i, and combining the weighted future damageestimates to generate 270 the fused future damage estimate at each lifeunit. The remaining useful life of the component is predicted 280 basedon the fused future damage estimate.

Consider a set of estimators that have diverse performances duringexecution. As discussed above, performance of an estimator can beencapsulated as error at a given life unit. In some embodiments, theweights are inversely proportional to the error of the estimator Onetechnique for determining weights is referred to as symmetric weightingof errors and is described below.

The error, e_(ni), for estimator n at life unit i is represented by theEquation 2:e _(ni)=(gt _(i) −y _(ni))  [2]

where gt_(i) is the current damage to the component (ground truth), andy_(ni) is the future damage estimate of the n^(th) estimator. Smoothingthe error to filter the higher order error such as by using a weightedaverage will yield less erratic results. In some embodiments, the filtermay be an exponential moving average filter with a smoothing constant,α, as set forth in Equation 3:e _(ni) =α*e _(ni-1)+(1−α)*e _(ni)  [3]The weight, w_(ni), for the n^(th) of N estimators at life unit i can beexpressed as the maximum of the errors for all the estimators e_(i) . .. e_(N) divided by the absolute value of the error for the estimator:

$\begin{matrix}{w_{ni} = \frac{\max\left( {e_{1i}\ldots e_{ni}} \right)}{❘e_{ni}❘}} & \lbrack 4\rbrack\end{matrix}$

As an illustrative example, consider the four estimator trajectories311, 312, 313, 314 as shown in FIG. 3 . The trajectories 311, 312, 313,314 represent the estimated damage to the component on the y axis duringa portion of the life of the component as measured by life units alongthe x axis. The x axis represents life units of the component in whichdamage progresses from some point where no damage is present to the endof life. The damage (ground truth) trajectory 319 is also plotted inFIG. 3 superimposed with the estimated damage trajectories 311, 312,313, 314 of estimators 1, 2, 3, and 4. The damage at end of life isindicated on the y axis of FIG. 3 . The estimated damage trajectories311, 312, 313, 314 of estimators 1 through 4 vary by how closely theyfollow the ground truth trajectory 319. In particular, the estimateddamage trajectories 311, 312, 313, 314 of estimators, 1, 2, and 3underestimate the life unit at which end of life occurs towards the end,predicting that the end of life occurs earlier than the ground truthtrajectory 319 at end of life. The estimated damage trajectory 314 ofestimator 4 overestimates the life unit at end of life, predicting thatthe end of life will occur at a later life unit than the actual end oflife according to the ground truth trajectory 319.

The estimated damage trajectory 314 of estimator 4 overestimates thedamage in the first part of the trajectory compared to the ground truthtrajectory and underestimates the damage nearer end of life. Thetrajectory 313 of estimator 3 has a relatively constant overestimationbias for damage with respect to the ground truth trajectory 319. Thetrajectory 312 of estimator 2 is close to the ground truth trajectory319 initially and then starts to overestimate the damage after aboutlife unit 65. The estimated damage trajectory 311 of estimator 1 alsostarts close to the ground truth trajectory 319, then deviates away.FIG. 3 also shows the fused estimated damage trajectory 318 superimposedwith the estimated damage trajectories 311, 312, 313, 314 of the fourestimators and the ground truth trajectory 319.

FIG. 4 shows the squared error e_(n)(i)²=(gt(i)−y_(n)(i))² for eachestimator trajectory as a function of remaining life. It will beappreciated from observation of FIG. 4 that the trajectory of estimator1 has an increasing squared error 411, the trajectory of estimator 2shows an initially small squared error 412 that subsequentlyexponentially increases, trajectory of estimator 3 shows a substantiallyconstant squared error 413, and the trajectory of estimator 4 hasinitial offset in squared error 414 that decreases, then increasesagain.

Symmetric weighting of errors according to Equations 2-4 for the fourestimators is shown in the superimposed graphs FIG. 5 . The weights forestimator 1 are initially high, then the weights drop off where thepredicted damage trajectory of estimator 1 (shown in FIG. 3 ) deviatesfrom the ground truth damage. The initially high weights for thepredicted damage trajectory of estimator 2 drops off due to theexponentially increasing error of the predicted damage trajectory forestimator 2. The weight for estimator 3 is initially low due to the biasof the predicted damage trajectory of estimator 3, but the weightincreases towards the end of life because of the relative accuracy ofthe predicted damage trajectory of estimator 3 with respect to theground truth increases—even though the bias is still there. The weightfor the predicted damage trajectory of estimator 4 is small initiallybecause the predicted damage trajectory shows a bias which thendisappears in mid-life. Even though the predictions of estimator 4underestimate the damage with respect to the ground truth damage at endof life, the performance of estimator 4 is relatively better compared tothe predictions of estimator 1 and estimator 2.

The weights are applied to the predicted damage estimates of estimators1, 2, 3, and 4 and the weighted predicted damage estimates are fused.For example, for each estimator, the weights at each life unit may bemultiplied by the predicted damage estimate for each estimator and thenthe weighted damage estimates are summed such that the fused result ateach life unit, i, is provided by Equation 5:

$\begin{matrix}{f_{i} = {\underset{n = 1}{\sum\limits^{N}}{w_{ni}*y_{ni}}}} & \lbrack 5\rbrack\end{matrix}$

The final fused damage estimate trajectory 318 as a function of lifeunit f(i) shown in FIG. 3 reflects a reasonable trade-off between thedifferent trajectories and approximates the ground truth reasonablywell. For prognostic applications, this is a desirable property. As canbe seen in Table 1, the root mean squared (RMS) error for the fusederror is considerably lower than the RMS of any individual estimator.

TABLE 1 Estimator RMS ERROR Estimator1 6.46 Estimator2 8.97 Estimator35.14 Estimator4 3.31 Fused estimator 1.12

In certain implementations, it is desirable to underestimate theremaining useful life of the component. Underestimation is often usefulwhen the fault mode towards the end of life has exponential propertiesthat may be associated with modalities that commence only towards thefinal stages of component life. Where this is the case, overestimationof remaining life may lead to failure before action can be taken.Therefore, in some embodiments, the process of obtaining the fuseddamage estimate ensure estimators that routinely overestimate remaininguseful life of the component are penalized more heavily than estimatorsthat underestimate the remaining useful life. In other words, earlyestimators (estimators that predict end of life at an earlier life unit)are weighted more heavily than late estimators (estimators that predictend of life at a relatively later life unit). Weighting the futuredamage estimates of estimators that underestimate remaining useful liferelatively more heavily and weighting the future damage estimates ofestimators that overestimate remaining useful life relatively lessheavily is referred to herein as asymmetric weighting.

Referring back to FIG. 1 , in asymmetric weighting, the weightcalculator 150 is configured to skew the weights toward underestimationof the remaining useful life. For example, the weight calculator 150 maycalculate relative larger weights for estimators that underestimate theremaining useful life and calculate relatively smaller weights forestimators that overestimate the remaining useful life.

In an embodiment, the asymmetric weighting can be realized by computingthe error for each estimator, n, at life unit, i, according to Equation6:

$\begin{matrix}{e_{ni} = {{\exp - {\frac{y_{ni} - {{ground}{truth}}}{{scalar}_{1}}{if}y_{ni}} - {{ground}{truth}}} < 0}} & \lbrack 6\rbrack\end{matrix}$ $\exp\frac{y_{ni} - {{ground}{truth}}}{{scalar}_{2}}$otherwise,

In general, scalar₁ can be any value and scalar₂ can be any value,however, in one particular embodiment, scalar₁=10 and scalar₂=0.4. Afterthe errors are calculated using Equation 6, the asymmetric weights maybe computed as in Equation 4. The weights are applied to the futuredamage estimates of the estimators and the weighted future damageestimates for all estimators are fused life unit-by-life unit to producethe fused future estimated damage trajectory. Trajectories 311, 312,313, 314 of the future damage estimate trajectories of estimators 1, 2,3, and 4, the current damage 319, and the fused estimated damagetrajectory 618 according to the asymmetric weighting approach are shownin the superimposed graphs in FIG. 6 . As can be seen in FIG. 6 , thefused damage estimate 618 produced by asymmetric weighting gravitatestowards the future damage estimate trajectory 314 of estimator 4 nearthe end of life, due to the higher weighting of the future damagetrajectory 314 of estimator 4 for negative error.

In this example, according to the ground truth trajectory 319, end oflife occurs when the damage has a value of 2.7 at life unit 100. Fromobservation of FIG. 6 , it is apparent that estimators 1, 2, 3 near endof life are early estimators because the life unit at which sufficientdamage to cause end of life (Damage=2.7) occurs at an earlier life unit(<100) than the end of life of the ground truth trajectory 319. Forestimator 1, damage reaches 2.7 at about life unit 90; for estimator 2,damage reaches 2,7 at about life unit 94; and for estimator 3, damagereaches 2.7 at about life unit 96. Estimator 4 is an estimator thatmakes late estimates because the life unit at which estimator 4 predictsend of life occurs at a later life unit (>100) than the end of life ofthe ground truth trajectory 319.

FIG. 7 shows superimposed graphs of the squared errors for each futuredamage estimate trajectories calculated using Equation 6. FIG. 8 shows azoomed in portion of the graphs of FIG. 7 .

After the errors are calculated using Equation 6, the asymmetric weightsmay be computed as in Equation 4. Superimposed graphs of the asymmetricweights 811, 812, 813, 814 are shown respectively in FIG. 9 forestimators 1-4. The weights 811 for the future damage estimates ofestimator 1 start out relatively high and then decrease toward the endof life because estimator 1, compared to the other estimators, makesestimates that are initially relatively premature about the futuredamage early on and the estimates become relatively more late nearer theend of life. The weights 812 for the future damage estimates ofestimator 2 again start out relatively high and then begin a sharpdecrease at about life unit 65. Estimator 2 makes estimates that areinitially rather premature but the estimates becomes quite late towardthe end of life. The weights 813 for estimator 3 remain relativelyconstant, increasing slightly toward the end of life. Estimator 3 tracksthe ground truth fairly closely but its estimates are slightly more latetoward the end of life. The weights 814 for estimator 4 start outrelatively low but rise with increasing life units as estimations fromestimator 4 becomes increasingly early.

The weights are applied to the future damage estimates for each of theestimators and the fused future damage estimate is determined. The fusedfuture damage estimate is shown as one of the superimposed graphs ofFIG. 6 .

The RMS error for the future damage estimates of estimators 1-4 and thefused future damage estimate are shown in Table 2. Again, the fusedfuture damage estimate shows a better result than any individualestimator alone. Compared to the results using symmetric error, theperformance has decreased slightly, likely owing in part to thestochastic nature of noise and the forced bias of the fused ensemblewhich is not reflected in the error calculation (which is symmetric).

TABLE 2 Estimator RMS ERROR Estimator1 6.57 Estimator2 8.85 Estimator35.15 Estimator4 3.35 Fused estimator 1.52

Referring again to FIG. 1 , according to some embodiments, the system100 includes a memory 180 that stores knowledge of the amounts ofunderestimation of the future damage at end of life by one or more earlyestimation models and/or knowledge of the amounts of overestimation ofthe future damage at end of life by one or more late estimation models.In these embodiments, the weight calculator 150 is configured tocalculate weights based on the knowledge of overestimation orunderestimation.

In some embodiments, prior to generating the future damage estimates,the estimation processor 110 is configured to update at each life unitat least some of the estimation models based on the magnitude of currentdamage as obtained from the damage estimator 130. In other words, theoperation of each estimator is modified based on the magnitude of thecurrent damage so that at the next life unit a more accurate predictionof future damage is obtained. This implementation can be described as“re-grounding” one or more of the estimators at each life unit based onthe magnitude of the current damage.

The system 100 can include a training subsystem 195 that trains theestimators 111, 112, 113, 114 to produce future damage estimates for thecomponent 199. The training subsystem 195 may train the estimators 111,112, 113, 114 using a machine learning approach, e.g., supervised orunsupervised machine learning. In some scenarios, the training subsystem195 may be configured to train one of more of the estimators 111, 112,113, 114 to underestimate the remaining useful life of the component.Alternatively or additionally, the training subsystem 195 may beconfigured to train one of more of the estimators to overestimate theremaining useful life of the component.

In some embodiments, weight assignment may be based on stage of life asillustrated by the embodiments described below. The stages of life maybe determined by the stage of life estimator 185 shown in FIG. 1 basedon direct or indirect inputs from the estimation processor 110, damagedetector 120, damage estimator 130, models 170 and/or other componentsof the system 100. When the weight assignment is based on stage of life,the life of the component 199 is divided into two or more stages.Different weighting processes may be used for different life stages. Forexample, one stage of life may use a symmetric weighting scheme whereasanother stage of life may use an asymmetric weighting scheme. Otherweighting schemes could alternatively be used.

In many implementations, the error at any particular life unit is notknown because the damage (namely, time to failure) at that life unit isonly available a posteriori. In this scenario, weights may be assignedusing an estimated error based on understanding the historical errorcharacteristics during a particular phase of end-of-life.

In some implementations it can therefore be helpful to methodicallydivide the remaining useful life of a component into different stages.For example, the life of the component could be divided into the stages“early”, “middle” and “late”. For example, the “early” stage can bedefined as the stage of life that has not exhibited adegradation-accelerated fault. It is the “normal” operating regime inwhich the component undergoes ordinary wear, typically manifested bysmall (often quasi-linear) changes in the performance parameters.

The “middle” stage, in contrast, is the period after a fault has beendetected in the component. In the system 100 of FIG. 1 , faultconditions are identified by the reasoner processor 190. In somescenarios, a fault condition may be manifested by a univariate symptomdetectable based on a single measurement by the damage detector 120 of aperformance parameter. In some scenarios, a fault condition may bedetectable based on multivariate symptoms extracted by the reasonerprocessor 190 through advanced signal processing and identified viaeither model-based diagnostics (e.g., based on one or more models 170 ofthe component 199) or data-driven diagnostics (e.g., based on one ormore performance parameters detected by the damage detector 120). Insome embodiments, no assumptions are made with regards to how the faultcondition was initiated. That is, the detected fault could be a faultcondition that worsened and exceeded an observable threshold or it couldbe a discrete and relatively sudden change that can be observed in thesensor data (or features of the sensor data). The features arecalculated to optimally accentuate the fault detectability using typicalmethods found in the domain. Depending on application, these could be acombination of sensor measurements (for example derived throughprinciple component analysis) or features obtained from the sensor databy transposing vibration data into the frequency domain through Fouriertransforms.

The last stage of component life, denoted the “late” stage, ischaracterized by an exponentially changing damage signature of thefuture damage trajectory (where the damage signature includes alldescriptions of either features of the trajectory (e.g., peaks,derivative, rise time, etc.), or future estimate values of thetrajectory. In some scenarios, the late stage may not be well separatedfrom the “middle” stage. In these scenarios, the late stage can bedefined by a subjective threshold that separates it from the middlestage. One motivation for separating the late stage from the middlestage stems from the realization that often times, additional faultmechanisms drive the very late stage of component faults. However, thesemechanisms are often times not included in the estimators, thereforeresulting in poor performance of the estimators towards the end of life.As a guideline, setting the threshold for the “late” stage might attemptto capture the last 15% of life after a fault has been first detected.In some scenarios, the process used to determine the stages of life doesnot have a priori access to ground truth, so it needs to be trained tocue off a relevant measurable performance parameter or a computable“health” parameter. For example, the future damage estimates generatedby the estimators 111, 112, 113, 114 can be considered to be healthparameters.

In accordance with some embodiments, the early life stage is a stage inwhich the component has not experienced a fault condition that exceeds afirst predetermined fault threshold. The middle life stage is a stage inwhich the component has experienced a fault condition that exceeds thefirst fault threshold. The late life stage is a stage in which thecomponent has experienced a fault condition that exceeds a secondpredetermined fault threshold.

FIG. 10 illustrates early, middle, and late life stages superimposed onthe trajectories 311, 312, 313, 314 of future damage estimates of theestimators 1-4, the ground truth damage trajectory 319, and the fusedfuture damage estimate trajectory 1018. FIG. 10 shows the life stagetracker 1017 where damage value 0 means “early” life, damage value 1 isinterpreted as “mid-level”, and damage value 2 means “late”. In thisparticular case, the classification of the stage values is based on thetrajectory 312 of estimator 2 as a driver where the mid-level damagethreshold was set to 0.5 damage units and the late threshold was set to1.5 damage units. It will be understood that other threshold valuescould be used for other scenarios. Note also that for this particularcase, the stage assignment for the “late” stage occupies more time thanthe “middle” stage. For the purpose of this example a larger “late”stage is more desirable and reflects the preferences for a particularsolution. It is conceivable that in some scenarios, the middle stage orlate stage weight assignments may be completely absorbed by theirneighboring stage. That is, there may be no middle stage or no latestage.

FIG. 11 shows the weights which reflect the average approximate weightcalculated via the asymmetric error assignment method described above.Superimposed graphs of the asymmetric weights are shown in FIG. 9 forestimators 1-4. Estimator 1, compared to the other estimators, makesestimates that are initially relatively premature about the futuredamage and its estimates become relatively more late at mid and latelife. Thus, the weights for the future damage estimates of estimator 1start out relatively high during early life and then decrease at thebeginning of middle life. Decreased weights continue during middle andlate life.

Estimator 2 makes estimates that are relatively early during early lifebut they become the most late of all the estimators toward the end oflife. The weights for the future damage estimates of estimator 2 againstart out relatively high and then sharply decrease at the beginning ofmiddle life. The weights for estimator 2 again sharply decrease at thebeginning of late life. These decreases in weights reflect the optimismof estimator 2 during these life stages.

Estimator 3 is the estimator that initially most closely tracks theactual damage (ground truth) but the estimates become earlier at end oflife. The weights for estimator 3 are low initially but increaseslightly at the beginning of middle life and increase again slightly atthe beginning of late life.

Estimator 4 is the estimator with the earliest estimates at the latestage of life although its estimates are fairly late during early life.The weights for estimator 4 start lower during early life but increasesignificantly at the beginning of middle life and increase significantlyagain at the beginning of the late life stage due to the risingpessimism of estimator 4.

FIGS. 12A and 12B are flow diagrams that illustrate a process fordetermining remaining useful life of a component in accordance with someembodiments. Referring to the flow diagram of FIG. 12A and the systemblock diagram shown in FIG. 1 , the estimation processor (110, FIG. 1 )implements 1210 a diverse set of estimators (111, 112, 113, 114, FIG. 1) that each generate future damage estimates for the component (199,FIG. 1 ). A stage of life estimator (185, FIG. 1 ) determines 1220 thelife stage of the component (199, FIG. 1 ). If the stage of lifeestimator determines 1221 that the stage of life is early life, thenearly life stage weights are calculated by the weight calculator (150,FIG. 1 ). If the stage of life estimator determines 1222 that the stageof life is middle life, then middle life stage weights are calculated bythe weight calculator (150, FIG. 1 ). If the stage of life estimatordetermines 1223 that the stage of life is late life, then late lifestage weights are calculated by the weight calculator (150, FIG. 1 ).The future damage estimates of all the estimators (111, 112, 113, 114FIG. 1 ) are fused by applying 1230 the weights respectively to thefuture damage estimates at each life unit, i, and combining the weightedfuture damage estimates to generate 1240 the combined future damageestimates for each life unit. The remaining useful life of the componentis predicted 1250 based on the combined future damage estimates.

FIG. 12B is a flow diagram that provides additional details about theoperation of the reasoner processor (190, FIG. 1 ) in accordance withsome embodiments. The stage of life processor 185 can be configured todetermine early, middle, and late life stages of the component (199,FIG. 1 ) based on detected or estimated fault conditions. Componentfault conditions may be determined by the reasoner processor 190 basedon information from the damage estimator (130, FIG. 1 ), the model (170,FIG. 1 ), the estimation processor (110, FIG. 1 ), and/or or othercomponents of the system 100. If no fault condition has been detected1260, or if a detected fault condition is below 1261 a first faultthreshold, then the early life stage weights may be calculated 1221.Optionally, the reasoner processor may confirm 1262 the presence and/oramount of the first fault condition to ensure that an actual faultcondition above the first fault threshold exists.

If the detected fault condition is above 1261 a first fault thresholdand below 1263 a second fault threshold, middle life weights arecalculated 1222. If the fault condition exceeds 1263 the second faultthreshold, late life stage weights may be calculated 1223. Optionally,the reasoner processor may confirm 1264 the presence and/or amount ofthe second fault condition to ensure that an actual fault conditionabove the second fault threshold exists. The weights are applied 1230 tothe future damage estimates provided by the estimators. A combinedfuture damage estimate is generated 1240 and used to predict 1250 theremaining useful life of the component.

According to some embodiments, the process used to calculate weights isbased on a prognostic horizon that represents the remaining useful lifeof the component. According to some embodiments, the reasoner processor(190, FIG. 1 ) is configured to detect a fault condition of thecomponent (199, FIG. 1 ). When a fault condition that is above athreshold is detected, the reasoner processor triggers the calculationof a prognostic horizon that represents the remaining useful life of thecomponent. Before the prognostic horizon the weight calculator may use afirst algorithm to determine the weights for the estimators. During theprognostic horizon, the weight calculator may use a second algorithm,different from the first algorithm, to determine the weight for theestimators. The fusion processor aggregates the weighted future damageestimates and predicts the remaining useful life of the component basedon the aggregated future damage estimate.

Referring again to FIG. 1 , according to some implementations, after thereasoner processor 190 detects the fault condition, the reasonerprocessor 190 then confirms the fault condition of the component beforetriggering the calculation of the prognostic horizon. For example, thereasoner processor 190 may delay a final confirmation of the faultcondition for additional units of time or cycles (life units) after thefault condition is initially detected to ensure that implementation ofthe prognostic horizon is based on an actual fault condition rather thannoise, outlier information or other spurious conditions. In somescenarios, the reasoner processor 190 detects the fault condition basedon a first source of information, e.g., information from the damagedetector 120, model 170 and/or damage estimator 130, and confirms thefault condition using a different source of information, e.g.,information from the future damage estimates of the estimators 111, 112,113, 114. In some scenarios, the reasoner processor 190 detects thefault condition based on the future damage estimates of the estimators111, 112, 113, 114 and confirms the fault condition using informationfrom the damage detector 120 and/or damage estimator 130.

FIG. 13 shows superimposed trajectories 311, 312, 313, 314 of the futuredamage estimates of the estimators, the ground truth 319, and the fuseddamage estimation trajectory 1318 calculated using first weights priorto a prognostic horizon and using second weights during the prognostichorizon period. FIG. 14 shows superimposed graphs of the weights used todetermine the fused estimation trajectory 1318 of FIG. 13 .

In some scenarios, the operating conditions of the component 199 maychange. The reasoner processor 190 may be configured to update theprognostic horizon based on changes in the anticipated load conditionsof the component 199, the anticipated environmental conditions of thecomponent 199, and/or other conditions that could affect the lifespan ofthe component 199. Information about the anticipated load conditions,the anticipated environmental conditions and/or other lifespan-affectingconditions may be obtained from operator or mission schedules or typicaloperational cycles that describe a process, for example. According tosome embodiments where the magnitude of current damage, gt_(i), can beobtained by the damage estimator 130, the prognostic horizon may beupdated based on the magnitude of current damage. In situations wherethe prognostic horizon is updated, the weights may also be updatedaccordingly. In some embodiments, the life stages, e.g., early, middle,late, are determined to occur after the prognostic horizon. In thesesituations, where the prognostic horizon is updated, the weights mayalso be updated to correspond to the updated life stage.

Various modifications and alterations of the embodiments discussed abovewill be apparent to those skilled in the art, and it should beunderstood that this disclosure is not limited to the illustrativeembodiments set forth herein. The reader should assume that features ofone disclosed embodiment can also be applied to all other disclosedembodiments unless otherwise indicated. It should also be understoodthat all U.S. patents, patent applications, patent applicationpublications, and other patent and non-patent documents referred toherein are incorporated by reference, to the extent they do notcontradict the foregoing disclosure.

The invention claimed is:
 1. A system comprising: an estimationprocessor configured to implement a set of diverse estimation modelsthat generate future damage estimates for a component, each estimationmodel using a different algorithm to generate a different future damageestimate, y_(i), for the component at each of a plurality of life units,i, of the component based on one or more anticipated stressors of thecomponent, wherein the anticipated stressors include at least one ofanticipated load conditions and anticipated environmental conditions,wherein the future damage is a prediction by each different algorithm ofincremental degradation to the component that accumulates in the futuredue to the anticipated stressors; at least one damage detectorconfigured to detect damage to the component; a damage estimatorconfigured to estimate a magnitude of current damage, gt_(i), of thecomponent for each life unit, i, based at least in part on the detecteddamage, the current damage being a current accumulation of degradationto the component; an error processor configured to estimate an error,e_(i), between each future damage estimate, y_(i), and the magnitude ofcurrent damage, gt_(i), for each life unit, i; a weight calculatorconfigured to calculate weights, w_(i), for the future damage estimates,each weight being inversely proportional to the error; and a fusionprocessor configured to: combine the future damage estimates of theestimation models by: applying the calculated weights respectively tofuture damage estimates providing weighted future damage estimates; andgenerating a combined future damage estimate by combining the weightedfuture damage estimates; and predict a remaining useful life of thecomponent based on the combined future damage estimate.
 2. The system ofclaim 1, further comprising at least one model of the component, whereinthe magnitude of current damage is based on estimations obtained fromthe model.
 3. The system of claim 1 wherein at each life unit, i, theweight, w_(i), for the future damage estimate, y_(i), is a maximum oferrors of future damage estimates of the estimation models 1 to N,max(e₁, e₂, . . . , e_(N)) for the life unit, i, divided by an absolutevalue of the error, |e_(i)| for the future damage estimate, y_(i). 4.The system of claim 1, wherein the weight calculator is configured toskew the weights toward underestimation of the future damage.
 5. Thesystem of claim 1, wherein the weight calculator is configured to:calculate relatively larger weights for damage estimates of estimationmodels that underestimate the remaining useful life of the component;and calculate relatively smaller weights for damage estimates ofestimation models that overestimate the remaining useful life of thecomponent.
 6. The system of claim 1, further comprising a memory thatstores knowledge of overestimation of future damage by at least oneestimation model and knowledge of underestimation of future damage by atleast another estimation model.
 7. The system of claim 1, wherein, priorto generating the future damage estimates, the estimation processor isconfigured to update at each life unit at least some of the estimationmodels based on the magnitude of current damage to obtain a moreaccurate prediction at a next life unit.
 8. The system of claim 1,further comprising a stage of life estimator configured identifymultiple stages of life of the component and to calculate the weightsbased on a current stage of life of the component.
 9. The system ofclaim 8, wherein the stage of life estimator is configured to identify:an early life stage in which the component has not experienced a faultcondition that exceeds a first fault threshold; and later life stages inwhich the component has experienced a fault condition that exceeds apredetermined fault threshold.
 10. The system of claim 1, furthercomprising a reasoner processor configured to detect a fault conditionof the component, wherein the reasoner processor is configured tocalculate a prognostic horizon of the remaining useful life of thecomponent in response to the fault condition.
 11. The system of claim10, wherein the fault condition is detected based on a first source ofinformation, and wherein the reasoner processor confirms the faultcondition of the component before triggering the calculation of theprognostic horizon from a second source of information different fromthe first source of information.
 12. The system of claim 10, wherein theweight calculator is configured to calculate the weights based on one orboth of the prognostics horizon and a life stage of the component withinthe prognostic horizon.
 13. The system of claim 12, wherein the reasonerprocessor is configured to update the prognostic horizon based onchanges in anticipated stressors of the component and/or the magnitudeof current damage, gt_(i), and wherein the weight calculator isconfigured to calculate the weights based on one of multiple stages oflife of the component that occur after the updated prognostic horizon.14. The system of claim 1, further comprising a training subsystemconfigured to train at least one estimation model to overestimate theremaining useful life of the component and/or to underestimate theremaining useful life of the component.
 15. The system of claim 1,wherein: the estimation processor is configured to detect outlier futuredamage estimates; and the weight calculator is configured to set theweights corresponding to the outlier future damage estimates to zero.16. A method comprising: implementing a set of diverse estimation modelsthat generate future damage estimates for a component, each estimationmodel using a different algorithm to generate a different future damageestimate, y_(i), for the component at each of a plurality of life units,i, based on one or more anticipated stressors of the component, whereinthe anticipated stressors include at least one of anticipated loadconditions and anticipated environmental conditions, wherein the futuredamage is a prediction by each different algorithm of incrementaldegradation to the component that accumulates in the future due to theanticipated stressors; detecting damage to the component; estimating amagnitude of current damage, gt_(i), of the component for each lifeunit, i, based at least in part on the detected damage, the currentdamage being a current accumulation of degradation to the component;estimating an error, e_(i), between each future damage estimate, y_(i),and the magnitude of current damage, gt_(i), for each life unit, i;calculating weights, w_(i), for the future damage estimates, each weightbeing inversely proportional to the error; and combining the futuredamage estimates comprising: applying the calculated weightsrespectively to future damage estimates providing weighted future damageestimates; and generating a combined future damage estimate by combiningthe weighted future damage estimates; and predicting a remaining usefullife of the component based on the combined future damage estimate. 17.The method of claim 16, wherein calculating the weights comprisesskewing the weights toward overestimation of the future damage.
 18. Themethod of claim 16, further comprising: identifying multiple stages oflife of the component, and calculating the weights based on a currentstage of life of the component.
 19. The method of claim 16, furthercomprising triggering a prognostic horizon of the remaining useful life,wherein calculating the weights comprises calculating the weights duringthe prognostic horizon.
 20. The system of claim 1, wherein the at leastone damage detector detects the damage to the component via a sensorthat senses a performance parameter of the component, and wherein thedamage estimator estimates the magnitude of the current damage based ondetermining a trend of the performance parameter and estimating thecurrent damage based on the trend.
 21. The system of claim 1, whereinthe damage estimator estimates the magnitude of the current damage basedon a physics model of the component, taking into account: past andpresent load conditions of the component; and past and presentenvironmental conditions of the component.